WPS7104
Policy Research Working Paper 7104
Transitioning from Low-Income Growth
to High-Income Growth
Is There a Middle Income Trap?
David Bulman
Maya Eden
Ha Nguyen
Development Research Group
Macroeconomics and Growth Team
November 2014
Policy Research Working Paper 7104
Abstract
Is there a “middle income trap”? Theory suggests that the at any particular middle income level. However, it does
determinants of growth at low and high income levels find evidence that the determinants of growth at low and
may be different. If countries struggle to transition from high income levels differ. These findings suggest a mixed
growth strategies that are effective at low income levels to conclusion: middle-income countries may need to change
growth strategies that are effective at high income levels, growth strategies to transition smoothly to high-income
they may stagnate at some middle income level; this phe- growth strategies, but this can be done smoothly and
nomenon can be thought of as a “middle income trap.” does not imply the existence of a middle income trap.
This paper does not find evidence for (unusual) stagnation
This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger
effort by the World Bank to provide open access to its research and make a contribution to development policy discussions
around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors
may be contacted at dbulman@worldbank.org, meden@worldbank.org and hanguyen@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Transitioning from Low-Income Growth to High-Income Growth:
Is There a Middle Income Trap?
David Bulman, Maya Eden, Ha Nguyen 1
JEL: O4
Key words: Middle Income Trap, Determinants of Growth
1
This paper is a part of the World Bank’s participation in G20 Summits. We thank the support from
DECOS. We thank Aart Kraay, Zia M. Qureshi and Luis Serven, for very helpful comments. All errors
are our own.
1. Introduction
Concerns about the so-called “middle income trap” have recently emerged among many middle income
countries. These countries worry that they may become “trapped” at the middle income level, and that
existing policies that facilitated earlier growth from low income to middle income might not work as well
for transitioning from middle income to high income. More broadly, middle income countries seek a set
of policies that can help them achieve strong and sustained growth and eventually help them join the
league of high income countries.
Finding a set of appropriate pro-growth policies is always a complicated task, especially because every
country faces its own institutional constraints. This paper does not lay out specific policy
recommendations; rather, it provides a set of stylized facts about middle income countries and about
fundamentals that might facilitate the transition from middle to high income. We focus on changes in
relative income (i.e., how countries catch up to other high income countries), rather than absolute income.
We find mixed evidence regarding the existence of a middle income trap—i.e., slowing growth that might
cause middle income countries to stagnate prior to joining the high income group. The predominant
evidence against the existence of such a middle income trap comes from an examination of the growth
paths of successful transitions. We find that “escapees” – countries that “escaped” the middle income trap
and became rich – tend to grow fast and consistently to high income, and do not stagnate at any point as a
middle income trap theory would suggest. In contrast, “non-escapees” tend to have low growth at all
levels of income. In other words, while the existence of a middle income trap implies that growth rates
systematically slow down as countries reach middle-income status, no such systematic slowdown is
apparent in the data.
Among middle income countries, descriptive analyses suggest the following factors associated with
higher growth: (1) economic structure, and in particular a faster transformation from agriculture to
industry; (2) higher export shares; (3) lower inflation; and (4) decreases in inequality and dependency
ratios. At the same time, we find some evidence that the effectiveness of different growth strategies may
vary across income levels. This is consistent with middle income trap theories suggesting that middle
income countries get stuck in the transition from growth strategies that are effective at low income levels
to growth strategies that are effective at high income levels. While we do not find evidence of being
“stuck,” we do find evidence that such a transition may be needed. We find that TFP growth is a much
larger source of economic growth, both in absolute and relative terms, in middle and high income
2
countries than in low income countries. 2 This highlights the limits of capital accumulation (after all,
investment has a decreasing marginal return) and suggests the important roles of education, research and
innovation, and structural reforms. Figure 1 presents average contributions to annual growth in output per
worker, by income level. 3 The orange part represents average annual TFP growth and the blue part
represents average annual growth of the capital-labor ratio (multiplied by the capital share). For low
income countries, the overwhelming majority of growth comes from capital accumulation. For middle
and high income countries however, the share of TFP growth is much larger.
6%
Average per capita Y/L growth
4%
2%
0
Low Income Lower Middle Upper Middle High Income
Income Income
α∙K/L TFP
Figure 1: Average contributions to growth, by income level
The observation makes sense. For low income countries, since the level of capital is still low, it is
relatively easier to attract and accumulate more capital (think of giving farmers tractors). When the level
of capital accumulation is higher, it is harder to attract investment because the return to capital now
becomes lower (i.e. it does not help to give one farmer two tractors). To maintain growth, countries have
to turn to other sources: better technologies, better management practices, and research and innovation.
Our conjecture is that countries with better strategies to access or, even better, generate state-of-the-art
technologies and management practices will be able to catch up with high income countries.
2
This is true regardless if human capital is included or excluded from the production function (see the Data
Appendix for more details).
3
Here, as in the rest of the paper, we define low income countries as those with per capita incomes less than 10% of
the US; middle income countries as those with per capita incomes between 10% and 50% of the US; and high
income countries as those with per capita incomes over 50% of the US. Here, we further divide middle income
countries into lower and upper middle income countries using a threshold of 30% of the US. Table A1 in the
Appendix lists countries by income group at 2009. The categorization looks reasonable.
3
While this aggregate finding is consistent with the above economic intuition, we fail to find strong
support for it on a more disaggregated level. We conduct a regression analysis of growth determinants in
low and middle income levels. Our findings do not support the hypothesis that innovation and human
capital accumulation are more important determinants of growth for middle income countries compared
to low income countries. Rather, the regressions suggest that growth of low and middle-income countries
may have to do more with the transformation of the economy: the growth effect of moving from
agriculture to industry is stronger for middle income countries than for low income countries, while the
growth effect of moving to services is weaker for middle income countries.
The paper is organized as follows: Section 2 reviews related literature on the middle income trap. Section
3 presents basic descriptions of income dynamics for a large set of countries, with a particular focus on
the middle income group. Section 4 contrasts middle income “escapees” and “non-escapees” along
several dimensions. Section 5 compares middle income country growth based on fundamentals. Section 6
presents regression results comparing growth determinants at middle income and low income levels.
Section 7 concludes.
2. Literature review
The term “middle income trap” first appeared in the World Bank’s “An East Asian Renaissance: Ideas for
Economic Growth,” which stated that “middle income countries…have grown less rapidly than either rich
or poor countries.” Since then, the concept of a middle income trap has become increasingly popularized
and discussed, 4 although a consensus on the validity of the concept has yet to emerge.
Early research has tried to document growth patterns for low and middle income countries. Pritchett
(2000) shows that the patterns for developing countries are best characterized as volatile. While some
countries have steady growth (hills and steep hills), others have rapid growth followed by stagnation
(plateaus), rapid growth followed by decline (mountains) or even catastrophic falls (cliffs), continuous
stagnation (plains), or steady decline (valleys). This suggests that econometric growth literature that
makes use of the panel nature of data is unlikely to be informative—a point previously made by Easterly
et al. (1993). In that paper, it is shown that growth is volatile across decades while country characteristics
are much more stable, and growth is largely driven by external shocks.
Following Pritchett’s (2000) suggestions, Hausmann, et al. (2005) look for instances of rapid but
sustained acceleration in economic growth. They find that growth accelerations tend to be correlated with
4
See, for example, The Economist June 23rd, 2011 for China’s “Middle Income Trap,” and other media coverage for
other countries (Russia, Malaysia and Latin Americas).
4
increases in investment and trade, and with real exchange rate depreciations. Growth accelerations are
also correlated with political regime changes and economic reforms. At the same time, growth
accelerations are highly unpredictable; a majority of reforms do not lead to growth acceleration.
Related to growth acceleration, recent literature analyzes growth slowdowns, with implications for the
future growth of China. Eichengreen et al. (2011) construct a sample of cases where fast-growing
economies slow down. They show that rapidly growing economies slow down significantly when their
per capita incomes reach around $US17,000 in year-2005 constant international prices. Since China will
soon reach this level of income, the paper implies that China will likely witness a slowdown. In a recent
paper, Cai (2012), through a discussion of many of China’s current problems, shares this concern. In our
own work, we do not find support for the hypothesis that growth slowdowns occur at any particular
income level. A potential reason for this difference is our use of relative, as opposed to absolute, income
levels.
More recently, Im and Rosenblatt (2013) discuss the definition of middle income traps. They explore both
the absolute and relative thresholds of the “traps”. With transitional matrix analysis, they also find little
support for the idea of middle income traps.
3. Basic facts on countries’ income dynamics
In this section we present some stylized facts on countries’ income dynamics. The sources of data are
presented in the appendix.
We divide countries into three groups: low income, middle income and high income. Low income, middle
income, and high income countries are those that have PPP GDP per capita less than or equal to 10%,
between 10% and 50% and above 50% of U.S. PPP GDP per capita. Table A1 in the appendix lists all
countries in the three categories in 2009 (in smaller bins).
We remove oil-exporters from our analysis for two reasons. First and more importantly, growth of these
countries is driven largely by oil exports and not so much by fundamentals. Second, proceeds from oil
exports can be very volatile, which would distort the persistence of countries’ relative income. See the
Data Appendix for the list of oil exporters removed from the sample.
5
6
2009 Log of % Income Relative to US
SGP
HK IRL
JPN
TWN
KOR ESP
PR GRC
SYC
4
2
0
0 2 4 6
1960 Log of % Income relative to the U.S
Figure 2: Relative income dynamics, 1960-2009
Figure 2 shows countries’ long run changes of their income relative to the U.S. The log of per capita
income relative to the U.S. in 1960 is on the x axis, with the 2009 value on the y axis. Each axis is divided
into three areas, representing the three income groups. Countries in the top-middle quadrant (in red) are
those that “escaped” from middle income to high income over this period. The list of escapees includes
Greece; Hong Kong SAR, China; Ireland; Japan; Puerto Rico; the Republic of Korea; Seychelles;
Singapore; Spain; and Taiwan, China. Two countries that nearly make the list (the top of the middle
quadrant) are Portugal and Cyprus, which are still classified as middle income in 2009. Table 1 below
summarizes the number of countries in 1960 (and alternatively, in 1970, where we have more data).
The predominant fact that emerges from Figure 2 and Table 1 is that relative income levels are highly
persistent. All high income countries in 1960 remained high income in 2009; a majority of middle income
countries remained middle income; 5 and only a handful of low income countries joined the middle
income group. A concern is that there might have been more fluid movements of countries between 1960
and 2009 - for example, some countries might have moved to high income and moved back, which Figure
1 would fail to capture. Figure A1 in the appendix shows that there are few such movements. Almost all
of the countries that have ever moved into high income have stayed there. Two exceptions are the Czech
Republic and Lebanon. 6 Another potential concern is that this persistence is an artifact of our selected
5
A few middle income countries declined to poor income groups (Bolivia, Ghana, Haiti, Honduras, Nicaragua,
Paraguay, and Zambia).
6
Czech Republic was a high income country (52% of the US) in 1990, but then it dropped to middle income
throughout the 1990s and early 2000s, and since 2005 it has again been high income. Lebanon became rich in the
mid-1970s but went to middle income in the early 1980s as it had a long civil war in the 1980s.
6
middle income threshold (i.e., 10-50% of US per capita GDP). Figure A2 in the appendix shows that this
is not the case: relative income mobility is no more persistent for middle income countries than other
countries regardless of the threshold selected. In fact, through low and middle income, income levels
become decreasingly persistent as countries get wealthier (i.e., low income levels are the most persistent,
lower middle income levels are slightly less persistent, and upper middle income levels are even less
persistent).
Base Year
1960 1970
# of countries in sample
Low income 42 59
Middle income 41 58
High income 19 26
Total 102 143
# (%) of income group transitions, base year to 2009
Low  Low 37 (88.1%) 50 (84.7%)
Low  Middle 5 (11.9%) 9 (15.3%)
Low  High 0 (0.0%) 0 (0.0%)
Middle  Low 7 (17.1%) 8 (13.8%)
Middle  Middle 24 (58.5%) 41 (70.7%)
Middle  High 10 (24.4%) 9 (15.5%)
High  Low 0 (0.0%) 0 (0.0%)
High  Middle 0 (0.0%) 1 (3.8%)
High  High 19 (100.0%) 25 (96.2%)
Table 1: Countries’ income distribution, 1960 and 1970
We also observe that escapees grow faster than non-escapees at all levels of income. Figure 3 shows the
average annual growth rates at different per capita income levels relative to the U.S. (shown in the x-
axis). The blue columns are the average growth rates for countries that ever escape from middle to rich,
and the red columns represent growth rates for those countries that never escape. The escapees do
consistently much better than their non-escapee counterparts, and they do not exhibit significant signs of
slowing down. In contrast, non-escapees have low and stable growth over all levels of income: they too
do not show signs of slowing down at middle income.
7
10% 8%
Average GDP Growth
2% 4% 0 6%
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Escapees Non-Escapees
Figure 3: Average annual change of PPP GDP per capita
Figure 3 presents evidence against the existence of a middle income trap that causes growth to stagnate at
a particular income level. Rather, non-escapees on average have slower growth at all levels of income,
suggesting a persistent role of country-specific constraints and policy problems. A very familiar graph
reinforces the point. Figure 4 shows the levels of PPP GDP per capita for escapees and some notable non-
escapees over time. One can see that escapees as a whole grow strongly towards high income and do not
see a “middle income trap”, while selected key non-escapees (Mexico, Malaysia, Brazil, Turkey) witness
relative stagnation for the entire period.
Another graph reinforces the notion that countries do not slow down at middle income levels (relative to
the U.S.). Figure 5 below shows a scatter plot of countries’ subsequent 10 year average growth against
(log of) countries’ initial income relative to the U.S. in 1960, 1970, 1980, 1990 and 2000. Evidence for a
middle income trap would imply a U-shaped curve, with countries systematically slowing down at middle
income levels. We do not see such evidence.
8
140
Singapore
120 Hong Kong
Ireland
Income relative to US (%) 100
Middle/high income Japan
threshold Spain
80
Taiwan
60 Greece
Korea
40
Mexico
20 Malaysia
Turkey
0
Brazil
1950
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
Figure 4: Income dynamics of escapees vs. non-escapees.
Note: The list of countries on the right hand side is sorted by 2009 income level
.15 .
Subsequent 10yr average
-.1 - 0 .05
0 1 2 3 4 5
Log of % income relative to US in 1960, 1970, 1980, 1990, and 2000
Figure 5: Initial relative income and growth
For escapees, GDP growth was high and sustainable-- strong growth in one period was followed by
strong growth in the subsequent period -- as if previous high growth paves the way for subsequent
growth. We refer to this as the “momentum” hypothesis. A look at all countries confirms empirically the
momentum hypothesis: there is a correlation between previous growth and current growth. Figure 6
shows the scatter plot of a middle income country’s average decadal growth rates in two consecutive
9
decades. The x axis presents average growth over t-10 through t-1, while the y axis presents average
growth from t+1 through t+10, for all available years. The orange dots correspond to those countries that
escaped from the middle income to the high income group. For all middle income countries, there is a
significant, positive correlation between lagged and current decadal growth rates. The correlation
coefficient for middle income escapees is 0.47, while the correlation coefficient for middle income non-
escapees is 0.25. 7 The positive correlation we find is particularly strong for escapees. Also for escapees,
the dots are much more concentrated at the upper right end of the scatter plot, implying that the escapees’
GDP growth is not only higher, but also more stable than other countries.
15%
10%
10 year future growth avg
5%
0%
-5%
-5% 0% 5% 10% 15%
10 year lagged growth average
Non-Escapees Non-escapee fitted values
Escapees Escapee fitted values
Figure 6: Growth correlation for middle income countries
There are several possible economic interpretations for the persistence in growth. For example, it is
possible that high growth provides more resources for investment (in infrastructure and education),
paving the way for high growth in the next period. Political economy may also play a role: it may be
politically easier for reforms to continue and deepen if they yielded economic success and high growth in
the previous period.
7
For all low and middle income countries, using non-overlapping decadal growth, the correlation coefficient
between lagged and future growth is 0.19. Easterly et. al. (1993) calculate the correlation coefficient between
1960s-1970s for developing countries as 0.1 and 1970s-1980s as 0.33
10
4. Comparing fundamentals of escapees and non-escapees
In this part we document several differences in fundamentals of escapees (middle income countries that
successfully transitioned to high income countries) and non-escapees (middle income countries that have
yet to transition).
In the graphs below, blue bars represent escapees from any base year, while orange bars represent non-
escapees. 8 In the appendix, figures A3-A12, results are presented for a similar exercise that disaggregates
escapee and non-escapee fundamentals across four middle income categories (10-20%, 20-30%, 30-40%,
and 40-50% of US income).
The basics and human capital
As seen in the left panel of Figure 7 below, escapees have higher growth as well as higher TFP growth.
Escapees exhibit higher levels of primary, secondary, and tertiary education, and are also clearly
differentiated from non-escapees by the number of patents they generate. Results presented in the
appendix suggest that tertiary education is more important for escapees at lower middle income levels,
while patents are more prevalent for escapees at upper middle income levels. This finding suggests that
the quality of education is more important at middle-high income levels, which is consistent with the view
that transition from middle to high income must be fueled by innovation-led growth.
Growth and TFP Years of education Patents
7% relative to US 4.5
6% 0.9 4
0.8 3.5
5%
0.7 3
4% 0.6
2.5
3% 0.5
2
0.4
2% 1.5
0.3
1% 0.2 1
0% 0.1 0.5
Per capita GDP TFP growth 0 0
growth Prim. Sec. Tert. Patents (1000)
Escapees Non-Escapees Escapees Non-Escapees Escapees Non-Escapees
Figure 7: Growth and TFP (left), years of education (center), and patents (right)
8
Unlike the previous list of escapees, which only included countries that “escaped” between 1960 and 2009, this list
includes countries that escaped earlier as well as countries for which we do not have 1960 data. These include:
Austria; Bahamas; Spain; Finland; Greece; Hong Kong SAR, China; Ireland; Iceland; Israel; Italy; Japan; Korea;
Macao; Malta; Puerto Rico; Singapore; Slovenia; Seychelles; and Taiwan, China.
11
Economic structure, openness, and macroeconomic conditions
Countries that escape seem to show a clear and rapid transition from agriculture to industry. As seen in
the appendix, this transition is particularly prevalent at lower-middle income levels. Escapees tend to
have larger industry sectors and smaller agriculture and service sectors, and they also have higher growth
in industry and lower growth in agriculture and services.
Escapees are in general more open and have better macroeconomic conditions. They are significantly
more export-oriented and have more undervalued currencies (defined as in Rodrik, 2008). This
undervaluation is particularly prevalent at lower middle income. Moreover, escapees, and particularly
upper-middle income escapees, do not experience high inflation. No middle income “eventual escapee”
ever experiences inflation over 20%, and by the time they reach upper-middle income levels they very
rarely experience inflation over 10%. The graph on the right below shows average annual inflation
calculated by excluding all inflation observations in the top 5%. 9 Even excluding the outliers, middle
income escapees experience lower inflation than non-escapees. However, they have higher levels of
external debt, which might be a result of greater access to outside markets or more financial development.
Due to a lack of data, we cannot include other financial development indices.
Sectoral Shares of GDP Growth in Sectoral Openness and Macro
60 Shares Conditions
0.02 0.7
50 0.6
0.01
0.5
40 0 0.4
30 -0.01 0.3
0.2
-0.02
20 0.1
-0.03 0
10
-0.04
0 -0.05
Agric. Industry Services Agric. Industry Services
Escapees Non-Escapees Escapees Non-Escapees Escapees Non-Escapees
Figure 8: Sectoral GDP share level (left) and growth (center), and openness and macro conditions (right)
9
The top 5% of inflation observations for non-escapee middle-income countries (of nearly 2000 observations)
includes all countries with inflation over 85.7%. The top 5% outliers are not driven simply by a few countries with
persistently high inflation (although many countries are indeed frequently delinquent); rather, 20 countries join the
list at some point (Argentina, Belarus, Brazil, Bulgaria, Costa Rica, Croatia, Ecuador, Estonia, Latvia, Lithuania,
Macedonia, Mexico, Peru, Poland, Romania, Russia, Suriname, Turkey, Ukraine, and Uruguay).
12
Governance, inequality, and demographics
Escapees and non-escapees have similar levels of democracy, however escapees have greater equality and
lower age dependency ratios. Escapees at all middle-income levels are also less likely to see increases in
inequality as well as decreases in the age dependency ratio (i.e., the so-called “demographic dividend”).
Governance indicators Inequality and Growth of inequality
(0-10) dependency and dependency
6 80 0.01
70 0.005
5
60
4 50 0
40 -0.005
3
30 -0.01
2 20
10 -0.015
1
0 -0.02
0 Gini coefficient Dependency Gini Dependency
Democracy Autocracy ratio coefficient ratio
Escapees Non-Escapees Escapees Non-Escapees Escapees Non-Escapees
Figure 9: Governance (left) and inequality and dependency levels (center) and growth (right)
To sum up, this descriptive analysis reveals that escapees have higher GDP and TFP growth at all relative
income levels. They have greater levels of human capital, experience a faster transformation to industry,
are consistently export-oriented, have better macroeconomic management, and have more income
equality and more growth-conducive demographic conditions.
5. Comparing middle income average growth based on fundamentals
The discussion in the previous section suffers from a potential methodological oversight. If we think of
escapees as rapidly growing countries, then the previous section basically showed that fast-growing
countries have better fundamentals than slow-growing countries. These associations could be very
misleading about the causal impact of the fundamentals. For instance, advocates like to point out that
fast-growing countries like Korea engaged in industrial policy, but this ignores the fact that many
countries have experimented with industrial policy without growing rapidly.
This section looks at growth performance based on fundamentals, rather than the reverse. In the
following discussion, each chart represents one potential determinant of future growth, similar to previous
sections. Here, the blue bars represent average growth trajectories (future 10-year average growth rates)
13
for countries with values below the median for that particular characteristic, and the orange bars represent
average growth trajectories for countries with values above the median. We separate results for lower
middle income countries (10-30% of U.S. income) and upper middle income countries (30-50% of U.S.
income) in order to see how growth determinants differ across the middle income spectrum. Medians are
calculated on an annual basis for lower and upper middle income countries separately. So, for instance, in
the first graph below, the blue bars represent the average future 10-year average growth rates for all
country/year observations in which the level of tertiary education is lower than the median value for all
lower/upper middle income countries in that particular year.
Human capital
Average years of tertiary education has little predictive power with regard to future growth; in the slight
(1/4 percentage point) differences, lower middle income countries benefit from more tertiary education,
while upper middle income countries seem to suffer. With regard to patents, a similar pattern emerges:
while lower middle income countries with above-median patents grow faster, upper middle income
countries grow slower. (This contrasts with the escapee vs. non-escapee chart, which shows upper middle
income level escapees with many more patents than non-escapees. The result in the previous chart was
driven by the patent performance of Korea and Japan, which is why this current exercise adds value.)
Average years of tertiary schooling relative to the US Number of patents (beginning of period)
.0
(beginning of period)
.0
.04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03
.03
.02
.02
.01
.01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 10: Tertiary education (left) and patents (right)
Economic structure
Lower-middle income countries that see larger declines in the agricultural share and increases in the
industry share grow much faster on average, while an increase in the share of the service sector translates
into slower growth; these trends hold for the upper middle income level, but are reduced in magnitude.
14
Growth in agricultural share of GDP Growth in industry share of GDP
(concurrent 10 year annual average) (concurrent 10 year annual average)
.05
.05
.04 .04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03 .03
.02 .02
.01 .01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 11: Agriculture share of GDP (left) and industry share of GDP (right)
Openness
For both lower and upper middle income countries, export-orientation is associated with higher growth
and undervaluation is associated with lower growth. These trends are particularly strong for upper middle
income countries.
Exports as a share of GDP (concurrent 10yr avg) Log undervaluation (concurrent 10yr avg)
.0 .05
.04 .04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03 .03
.02 .02
.01 .01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 12: Exports (left) and undervaluation (right)
Macroeconomic conditions
Countries with lower levels of inflation grow faster. Inflation itself is fairly persistent, so it is not
surprising that lagged and concurrent inflation both have negative predicted effects on growth. Countries
with lower external debt grow faster.
15
External debt as a share of GNI
CPI inflation (concurrent 10yr average) (concurrent 10 year average)
.0
.04
.04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03
.03
.02
.02
.01 .01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 13: Inflation (left) and external debt (right)
Governance and politics
Democracy has little if any predicted effect on growth for either lower or upper middle income countries.
Similar to the previous results, autocracy seems to help growth at lower middle income levels but harm
growth at upper middle income levels.
Democracy indicator (beginning of period) Autocracy indicator (beginning of period)
.0 .05
.04 .04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03 .03
.02 .02
.01 .01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 14: Democracy (left) and autocracy (right)
Inequality and demographics
Both the level of demographic characteristics at the beginning of the period and changes in demographic
characteristics over the period affect growth. Lower dependency ratios result in faster growth, and
declining dependency ratios (the “demographic dividend”) also translate into faster growth.
16
In terms of inequality, beginning-of-period levels are largely irrelevant, but larger increases in the Gini
coefficient are associated with slower growth, with a particularly pronounced effect for upper middle
income countries.
Age dependency ratio (beginning of period) Change in age dependency ratio (concurrent 10yr avg)
.05
.04
.04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03
.03
.02
.02
.01
.01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 15: Age dependency level (left) and change (right)
Gini coefficient (beginning of period) Change in Gini coefficient (concurrent 10yr avg)
.0
.05
.04
.04
Future 10 Year Growth Avg
Future 10 Year Growth Avg
.03 .03
.02 .02
.01 .01
0 0
Lower Middle Income Upper Middle Income Lower Middle Income Upper Middle Income
Below Median Average Above Median Average Below Median Average Above Median Average
Figure 16: Gini coefficient level (left) and change (right)
To sum up, the factors that stand out from the descriptive analysis in sections 4 and 5 as associated with
growth for middle income countries are: (1) economic structure, namely a faster transformation from
agriculture to industry; (2) export-orientation; (3) lower inflation and external debt; and (4) decreases in
inequality and the age dependency ratio.
6. Regressions
The differential growth performance suggests that there is room for a more systematic investigation. In
this section we do the following: (1) run a pooled OLS regression on middle income countries, to see
what factors have significant impacts on output growth, and (2) run a pooled OLS regression on middle
17
and low income countries, and interact the factors with a middle income dummy to identify which factors
matter for poor countries but not for middle income countries and vice versa. 10 While we are aware that
several issues exist with cross-country growth regressions (see Esterly et al, 1993), the regressions help
to provide some additional suggestive evidence for our exercises in section 5.
In all of the regressions, the dependent variable is the overlapping decade average growth of annual PPP
GDP per capita. To control for heteroscedasticity and within-country serial correlations in the error terms,
we report robust Newey-West type t-statistics. The nine right hand side variables are Gini coefficient
(concurrent average level), Fertility Rate (5 year lag average level), Age Dependency (concurrent average
change), Agriculture share of GDP (concurrent average change), Tertiary education level relative to the
U.S. (at the beginning of the 10-year period), Inflation (5 year lag average), Polity score (5 year lag
average), Trade share of GDP (concurrent average level), Log of undervaluation (concurrent average
level). Since many of these variables can be endogenously determined with growth, the results reported
below are best treated as associations. Please see the definition, the construction, and detailed data sources
in the Data Appendix.
Baseline regressions
The baseline regression pools low and middle income countries and looks at nine variables and their
interaction terms with the middle income dummy, along with controls for lagged income growth and
income relative to the United States (see table A2 in the Appendix). Along with the baseline regression,
table A2 also presents results using absolute income as a control; not including the control for lagged
growth; and regression results for both the low and middle income sub-samples, excluding the interaction
terms.
As suggested by the “momentum” argument, lagged five year growth is significant in all specifications.
This is consistent with the standard conditional convergence story, which suggests that growth rates of
developing countries should decline over time (thus implying a serial correlation in growth levels). But
the coefficient is nearly twice as large and much more significant for the middle income sub-sample than
for the low income sub-sample, implying that momentum may be more important for middle income
countries.
10
Our approach is related to Barreto and Hugh (2004). They show differential growth determinants for under-
achievers (i.e. countries that grow more slowly than traditional characteristics predict that it should) and over-
achievers.
18
Generally, the following factors are significant for growth of middle income countries: Gini coefficient
(at 10% level), fertility rate (at 10% level), decline in the agricultural share of GDP (at 5% level), and the
trade share of GDP (at the 10% level). We discuss the important indicators in more detail below.
Structural variables
In the baseline regressions, regardless of the controls or sample (low, middle, or both), the coefficient on
the change in the agricultural share of GDP is significantly negative, suggesting that declining agriculture
shares of GDP are important for growth (see Tables A2 and A3). The absolute coefficient for the low
income sub-sample is larger than that for the middle income sub-sample. However, the interaction term
(the middle income dummy) is not robustly significant.
Table A3 shows that industrial share of GDP has a significant positive effect on growth for middle
income countries, and but has insignificant differential impacts on low and middle income countries.
Interestingly, growth in the services share of GDP leads to a negative and significant coefficient on
growth in middle income countries. The interaction term is also significant and negative.
The regression results can be interpreted as follows: a decline in the share of agriculture or an increase in
industry share is positively associated with growth both in low and middle income countries. However,
growth in services actually harms growth in middle income countries. This is probably because services
in middle income countries are still of lower productivity than industry; an expansion of services at the
cost of manufacturing can actually hurt growth.
Human capital and Inequality
The lagged level of years of tertiary education is insignificant in the pooled sample, as is the interaction
term. This is consistent with existing literature: current measures of human capital have little effect on
growth. Similarly, higher inequality does not seem to have an impact on most of the sample, except for
middle income countries. It has a negative coefficient but is only significant at the 10% level.
Openness
In the full regressions, trade has a negative but insignificant coefficient. In the middle income sub-sample
however, trade has a slightly significant and positive association with growth. Similarly we find the
interaction term is positive, implying that trade has a stronger effect for middle income countries than low
income countries. Undervaluation, on the other hand, has little impact on growth. However, the
interaction term (the middle income dummy) is negative and significant, implying that the benefit of
undervaluation on growth (if any) is much smaller when a country is already a middle income country.
19
7. Conclusion
In this paper, we have attempted to answer two questions: Is there a middle income trap? If there is a
middle income trap, what causes it?
We answer the first question in the negative: countries that grow fast continue to grow fast, and they do
not get “stuck” at any particular middle income level. This suggests that becoming “trapped” in some
middle income level is not inevitable. However, this finding does not mean that there are no countries that
are trapped at a middle income level. Indeed, middle income countries that did not “escape” remain
stagnant with low growth at all levels of relative income. Relative income levels are highly persistent, and
transitioning from middle income to high income is hard.
Even in the absence of any evidence for a middle income trap, it is worth exploring the different
fundamentals of escapees and non-escapees, as well as the effects of different growth strategies at middle
income and low income levels. We find that common wisdom largely applies: escapees have higher
growth at all relative income levels, higher TFP growth, and experience a faster transformation towards
industry. They have better macroeconomic management and greater income equality, and they are
consistently more export-oriented. An alternative analysis focused on fundamentals also reveals that
faster transformation to industry, low inflation, stronger exports, and reduced inequality are associated
with stronger growth.
Cross-country growth regressions seem to confirm that growth in middle income countries is positively
associated with industrialization, openness, and equality. However, we do not see clear associations
between education and innovation to growth in middle and low income countries.
20
References
Barreto , Raul and Hughes, Tony (2004), “Under Performers and Over Achievers: A Quantile Regression
Analysis of Growth”, The Economic Record, (80), 17-35
Barro, Robert and Jong-Wha Lee, April 2010, "A New Data Set of Educational Attainment in the World,
1950-2010." NBER Working Paper No. 15902.
Caselli, Francesco, 2005. “Accounting for Cross-Country Income Differences.” In: Philippe Aghion &
Steven Durlauf (ed.), Handbook of Economic Growth, Edition 1, Volume 1, Chapter 9, pp. 679-741.
Cai, Fang (2012) Is There a “Middle-income Trap”? Theories, Experiences and Relevance to China,
China & World Economy / 49–61, Vol. 20, No. 1
Eichengreen, Barry, Donghyun Park, and Kwanho ShinWhen (2011), “Fast Growing Economies Slow
Down: International Evidence and Implications for China”, NBER Working Paper 16919
Easterly, William, Michael Kremer, Lant Pritchett, and Lawrence Summers, 1993. "Good policy or good
luck?: Country growth performance and temporary shocks," Journal of Monetary Economics, Elsevier,
vol. 32(3), pages 459-483
Hall, Robert and Charles Jones, February 1999. “Why Do Some Countries Produce So Much More
Output Per Worker Than Others?” The Quarterly Journal of Economics, Vol. 114(1), pp. 83-116.
Hausmann, Ricardo, Lant Pritchett, and Dani Rodrik, 2005. "Growth Accelerations," Journal of Economic
Growth, Springer, vol. 10(4), pages 303-329
Heston, Alan, Robert Summers and Bettina Aten, May 2011. Penn World Table Version 7.0, Center for
International Comparisons of Production, Income and Prices at the University of Pennsylvania.
Im, Fernando Gabriel & Rosenblatt, David, 2013. "Middle-income traps : a conceptual and empirical
survey," Policy Research Working Paper Series 6594, The World Bank.
Marshall, Monty G., 2011. “Polity IV Project: Political Regime Characteristics and Transitions, 1800-
2010.” http://www.systemicpeace.org/polity/polity4.htm.
Milanovic, Branko, 2005. Worlds Apart: Measuring International and Global Inequality. Princeton:
Princeton University Press, 2005. Data at: http://econ.worldbank.org/projects/inequality.
21
Pritchett, Lant (2000) Understanding Patterns of Economic Growth: Searching for Hills among Plateaus,
Mountains, and Plains, The World Bank Economic Review, Vol. 14, no. 2: 221-50
Rodrik, Dani, Fall 2008. “The Real Exchange Rate and Economic Growth.” Brookings Papers on
Economic Activity.
World Bank (2007), “An East Asian Renaissance: Ideas for Economic Growth”
World Bank, 2012. World Development Indicators.
22
Appendix
POOR (38.6%) MIDDLE (39.8%) RICH (21.7%)
0-2.5% 2.5%-5% 5-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90+%
Zimbabwe Mali Zambia Bhutan Serbia Chile Poland Malta Puerto Rico France United Kingdom Austria
Dem. Rep. of Congo Rwanda Sudan Kiribati Belize Belarus Estonia Barbados Slovenia Finland Denmark Switzerland
Burundi Benin Pakistan Fiji Botswana St. Lucia Cyprus Czech Republic Israel Japan Belgium Netherlands
Liberia Uganda Nicaragua Guyana Jamaica Latvia Slovak Republic Seychelles Greece Ireland Hong Kong United States
Somalia Tanzania Kyrgyzstan Maldives Mauritius St. Kitts & Nevis Portugal Korea New Zealand Germany Sweden Australia
Niger Timor-Leste Uzbekistan Egypt Brazil Greda Bahamas Taiwan Canada Macao
Eritrea Nepal Moldova Georgia Turkey Lebanon Italy Iceland Singapore
Central African Rep. Afghanistan Djibouti Armenia Dominican Republic Lithuania Spain Norway
Malawi Kenya Laos Jordan Panama Russia Bermuda
Guinea-Bissau Bangladesh Philippines Namibia Romania Palau Luxembourg
Mozambique Cote d'Ivoire Vietnam Dominica Surime Croatia
Ethiopia Lesotho Papua New Guinea Ecuador Costa Rica Antigua and Barbuda
Togo Haiti Syria Tunisia Uruguay Hungary
Madagascar Gambia, The India Guatemala Bulgaria
Sierra Leone Ghana Morocco El Salvador Cuba
Guinea Senegal Micronesia, Fed. Sts. Albania Malaysia
Comoros Mauritania Mongolia Samoa Mexico
Burkina Faso Cambodia Swaziland Vanuatu Argentina
Sao Tome and Princip Honduras Ukraine
Cameroon Bolivia Bosnia and Herzegovi
Tajikistan Paraguay Marshall Islands
Solomon Islands Cape Verde Montenegro
Indonesia China
Sri Lanka St.Vincent & Gredines
Peru
Colombia
South Africa
Macedonia
Thailand
Tonga
Table A1: Income categories, 2009
23
6
(t+10) log of % income relative to US
2 0 4
0 2 4 6
(t) log of % income relative to US
Figure A1: The log of per capita income relative to the US in time t is on the X axis, with the time t+10
value on the y axis. The dots correspond to every possible 10-year period between 1950 and 2009. The
countries in orange are those that ever escape from middle to high income at any point. The orange
countries in the middle-right quadrant (i.e., those that went from rich to middle and at some point also
went from middle to rich) are the Czech Republic (which got rich in the mid-1990s after dropping to
middle in 1990) and Lebanon (which got rich in the mid-1970s and then went to middle income in the
early-1980s).
24
100%
90%
Share of countries, by 10% income bin
80%
70%
60%
50%
40%
30%
20%
10%
0%
0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90+%
Income relative to the US
Stagnate (stay in same or move to lower income category) Grow (move to higher income category)
Figure A2: Income mobility at different income categories
25
Figures A3-A12:
In the following graphs, the blue bars represent escapees (i.e. Greece; Hong Kong SAR, China; Ireland;
Japan; Korea; Puerto Rico; Seychelles; Singapore; Spain; and Taiwan, China), while the orange bars
represent non-escapees. The countries are grouped on the x-axis by their income level relative to the US.
In general, the first orange bar, representing low income countries, should be ignored, as the bar only
represents figures for Taiwan, China, in 1951-53. The first blue bar is still useful as a comparison to low
income countries. Additionally, the escapees 10-20% of US income bar in many cases only includes
information for Korea; Taiwan, China; and/or Hong Kong SAR, China. The income bins between 20%
and 50% of US income tend to represent data for many more countries. The time period is 1951-2009.
Per capita GDP growth (%) TFP growth (%)
4
10
8 3
6
2
4
1
2
0 0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A3: Annual growth of PPP GDP per capita (left) and TFP growth (right)
Average years of primary schooling relative to US Average years of secondary schooling relative to US
.5
1
.4
.8
.3
.6
.2
.4
.1
.2
0
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A4: Primary education (left) and secondary education (right) relative to the US
26
Average years of tertiary schooling relative to US Number of patents
8,000
.3
6,000
.2
4,000
.1
2,000
0
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A5: Tertiary education relative to the U.S. (left) and Number of patents (right)
Agriculture as a share of GDP (%) Annual growth of agriculture as a share of GDP (%)
40 0
30 -2
20 -4
-6
10
-8
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A6: Agriculture share level (left) and growth (right)
Industry as a share of GDP (%) Annual growth of industry as a share of GDP (%)
4
40
3
30
2
20
1
10 0
-1
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A7: Industry share level (left) and growth (right)
27
Exports as a share of GDP (%) Log undervaluation
80 .4
60 .2
40 0
-.2
20
-.4
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A8: Export share of GDP (left) and undervaluation (right)
CPI inflation (%) External debt as a share of GNI (%)
15 80
60
10
40
5
20
0 0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A9: Inflation (left) and external debt (right)
Democracy indicator (0-10) Autocracy indicator (0-10)
8 8
6 6
4 4
2 2
0 0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A10: Democracy (left) and autocracy (right)
28
Age dependency ratio Growth in the age dependency ratio
0
80
-.005
60
-.01
40 -.015
20 -.02
-.025
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A11: Age dependency ratio level (left) and growth (right)
Gini coefficient Change in the Gini coefficient
50 .01
40 .005
30
0
20
-.005
10
-.01
0
0-10 10-20 20-30 30-40 40-50 0-10 10-20 20-30 30-40 40-50
Non-Escapees Escapees Non-Escapees Escapees
Figure A12: Gini coefficient level (left) and growth (right)
29
Absolute No lagged Low Middle
Baseline
GDP growth income income
Gini - concurrent avg level -0.000 -0.000 -0.000 -0.000 -0.000
(1.03) (1.29) (0.58) (1.25) (2.40)*
Fertility - 5yr lag avg 0.001 -0.000 0.001 0.000 0.003
(0.54) (0.13) (0.64) (0.16) (2.16)*
Dependency - concurrent avg change -0.179 -0.124 -0.245 -0.121 -0.144
(0.98) (0.67) (1.22) (0.56) (0.83)
Agr/GDP - concurrent avg change -0.321 -0.312 -0.378 -0.329 -0.181
(5.12)** (5.03)** (5.44)** (5.19)** (3.90)**
Tertiary - lag 1yr relative to US 0.001 0.001 -0.006 -0.005 0.005
(0.07) (0.10) (0.51) (0.37) (0.34)
CPI - 5yr lag avg 0.000 0.000 0.000 0.000 0.000
(1.44) (1.45) (0.01) (1.08) (0.67)
Polity score - 5yr lag avg 0.000 0.000 0.000 0.000 0.000
(1.45) (1.36) (1.12) (1.36) (1.76)
Trade/GDP - concurrent avg level -0.000 -0.000 -0.000 -0.000 0.000
(0.86) (0.89) (1.10) (1.02) (2.45)*
Log underval. - concurrent avg level 0.004 0.004 0.004 0.003 -0.008
(0.73) (0.71) (0.77) (0.50) (1.41)
Variables interacted with MI dummy
Gini - concurrent avg level -0.000 -0.000 -0.000
(1.24) (1.07) (1.56)
Fertility - 5yr lag avg 0.003 0.002 0.004
(1.37) (1.25) (1.80)
Dependency - concurrent avg change 0.009 0.019 -0.137
(0.04) (0.08) (0.49)
Agr/GDP - concurrent avg change 0.147 0.125 0.235
(1.94) (1.66) (2.89)**
Tertiary - lag 1yr relative to US 0.000 0.010 -0.003
(0.02) (0.56) (0.18)
CPI - 5yr lag avg -0.000 -0.000 -0.000
(0.88) (1.38) (2.07)*
Polity score - 5yr lag avg 0.000 0.000 0.000
(0.14) (0.51) (0.23)
Trade/GDP - concurrent avg level 0.000 0.000 0.000
(1.85) (2.04)* (2.04)*
Log underval. - concurrent avg level -0.014 -0.016 -0.022
(1.78) (2.04)* (2.71)**
Controls
Per capita GDP growth - 5yr lag avg 0.191 0.190 0.145 0.220
30
(6.06)** (6.09)** (3.03)** (5.46)**
Per cap. GDP (rel. US unless specified) -0.000 -0.000 -0.000 0.000 -0.000
(2.19)* (4.70)** (0.74) (0.17) (2.20)*
Constant 0.040 0.049 0.042 0.045 0.035
(4.77)** (5.73)** (4.63)** (3.07)** (3.55)**
Observations 1682 1682 1686 823 859
Sample L&M L&M L&M L M
Robust z statistics in parentheses; * significant at 5%; ** significant at 1%
Table A2: Main regression results
31
Concurrent 10yr growth of agriculture share of GDP
Full sample Low income Middle income
Variable -0.312 -0.320 -0.174
(4.25)** (4.18)** (4.97)**
MI*Variable 0.127
(1.60)
Lag 5yr avg growth 0.143 0.103 0.194
(4.25)** (2.08)* (7.61)**
Income rel to US -0.000
(3.49)**
Constant 0.037 0.036 0.028
(16.10)** (13.88)** (15.65)**
Observations 3898 2116 1782
Concurrent 10yr growth of industry share of GDP
Full sample Low income Middle income
Variable 0.072 0.073 0.197
(1.48) (1.48) (4.53)**
MI*Variable 0.124
(1.96)*
Lag 5yr avg growth 0.154 0.144 0.165
(5.04)** (3.10)** (6.48)**
Income rel to US -0.000
(1.21)
Constant 0.036 0.036 0.033
(16.53)** (14.34)** (21.48)**
Observations 3893 2111 1782
Concurrent 10yr growth of services share of GDP
Full sample Low income Middle income
Variable 0.093 0.098 -0.156
(1.68) (1.78) (3.79)**
MI*Variable -0.242
(3.57)**
Lag 5yr avg growth 0.159 0.151 0.166
(5.07)** (3.13)** (6.53)**
Income rel to US -0.000
(0.90)
Constant 0.037 0.036 0.035
(16.65)** (14.07)** (21.85)**
Observations 3893 2111 1782
Table A3: Growth with different structural variables
32
DATA APPENDIX
Exclusion of oil countries
We identify oil-rich countries as those whose average oil exports as a share of GDP exceed 30% or whose
oil rents as a share of GDP exceed 29%, using World Bank WDI data. With these criteria, oil exporters
are: Algeria, Angola, Azerbaijan, Bahrain, Brunei, Chad, Congo, Equatorial Guinea, Gabon, Iran, Iraq,
Kazakhstan, Kuwait, Libya, Nigeria, Oman, Qatar, Saudi Arabia, Trinidad and Tobago, Turkmenistan,
UAE, Venezuela, and Yemen.
International growth accounting exercise
Baseline data used for growth accounting exercise, including per capita GDP, employment, and
investment, come from the Penn World Tables Version 7.0 (Heston, et al., 2011).
Following Caselli (2005), capital stocks are generated using a perpetual inventory method:
Kt = It + (1 − δ)Kt−1,
where It is investment and δ is the depreciation rate. We assume 6% depreciation across countries. For
countries with available investment data pre-1970, we calculate the initial capital stock as:
K0 = I0/(g + δ),
where I0 is investment in its first available year and g is the average geometric growth rate of investment
between I0 and 1970. For those countries with investment data available starting only in the 1970s, we
calculate Kt and K0 with the same equations, but substitute g as the average geometric growth rate of
investment between I0 and 1980.
Human capital data at the primary, secondary, and tertiary level come from Barro and Lee (2011). This
data covers average years of schooling in the population over 15 years old from 1950-2010, in five-year
intervals. 11 Given the persistence of years of schooling data, we extrapolate data for intervening years by
assuming constant growth over each 5-year period. To generate a human capital index, we follow Hall
and Jones (1999) and generate a human capital index as:
h = eφ(s),
11
With our focus on middle income and lower income countries, and given lower tertiary attendance rates in these
countries, we focus on years of schooling in the 15+ population rather than the 25+ population, as did Caselli (2005)
and Hall and Jones (1999).
33
where s is average years of schooling and φ(s) is a piecewise linear function with slope contingent on
estimates for returns to different levels of schooling: 0.13 for s ≤ 4, 0.10 for 4 < s ≤ 8, and 0.07 for 8 < s. 12
In the baseline growth accounting exercise, we exclude human capital (its inclusion makes little
difference, as shown below) and adopt a simple Cobb-Douglas production function:
Y=AKαL1-α,
Where Y is GDP, K is the aggregate capital stock, L is the number of workers and α is a constant
representing factor shares. We then divide through by the number of workers:
y = Akα,
where y = Y/L and k = K/L. A represents the efficiency with which capital and labor are used, and thus
corresponds to total factor productivity (TFP). We take growth rates of y and k and then estimate TFP as:
TFP = gy – α*gk,
where the prefix g denotes annual growth rates. In this equation, following general practice, we set α =
0.33. As a robustness check, we also calculate TFP including the human capital measure in the growth
accounting exercise: 13
y = Akαh1-α
The TFP results across income levels are not greatly affected
Inequality
Inequality data on Gini coefficients comes from Milanovic (2005). Milanovic calculates a variable
“Giniall” that reports Gini coefficients from a wide range of nationally representative household surveys,
covering 1541 country/years. Given many missing observations and the relative annual persistence of
Gini coefficients, we replace this “Giniall” variable by its running 5-year average.
Governance
12
From Caselli (2005): “International data on education-wage profiles (Psacharopulos, 1994) suggests that in Sub-
Saharan Africa (which has the lowest levels of education) the return to one extra year of education is about 13.4
percent, the World average is 10.1 percent, and the OECD average is 6.8 percent. Hall and Jones’s measure tries to
reconcile the log-linearity at the country level with the convexity across countries.”
13
Here, h is the human capital measure described above, and can be seen as the human capital per worker; in other
words, it is the “quality adjusted” workforce, Lh, divided by the number of workers.
34
Governance indicators come from the Polity IV database. The democracy and autocracy indicators are
composite variables (“DEMOC” and “AUTOC” in the original dataset) based on an additive 11 point
scale (0-10). The included indicators for both composite variables can be found at
http://www.systemicpeace.org/inscr/p4manualv2010.pdf. The full “polity” score is calculated by
subtracting a country’s autocracy value from its democracy value.
Undervaluation
Undervaluation is calculated following Rodrik (2008), where a “real” exchange rate is calculated as the
actual exchange rate divided by the PPP conversion factor, using the PWT data. Unlike Rodrik, this
index is calculated on an annual as opposed to 5-year basis. Given the Balussa-Samuelson effect,
whereby non-traded goods are cheaper in poorer countries, Rodrik generates estimated real exchange
rates by regressing the log of the real exchange rate on log per capita GDP, including fixed effects for the
time period. The undervaluation index is calculated as the difference between the log real exchange rate
and the fitted values from this regression (which correspond to the Balussa-Samuelson estimated real
exchange rates). The undervaluation index is in log form, and positive values indicate higher levels of
undervaluation.
Other data
Additional data comes from the World Bank World Development Indicators dataset, and generally
requires no explanation. A full list of variables and data sources is presented in the table below for all
variables whose calculation is not described above.
Variable Source
GDP and GDP growth PWT 7.0
Employment PWT 7.0
Investment PWT 7.0
Years of schooling Barro and Lee (2011)
Gini coefficient Milanovic (2005)
Agriculture share of GDP World Bank
Industry share of GDP World Bank
Services share of GDP World Bank
CPI inflation World Bank
35
Patents World Bank
Fertility World Bank
Age dependency ratio World Bank
Exports/GDP World Bank
Trade/GDP PWT 7.0
Democracy indicator Polity IV
Autocracy indicator Polity IV
Polity score Polity IV
36